Bibliographic record
Abstract
In the twenty-eighth edition of How Ottawa Spends leading Canadian scholars examine the Harper government agenda in the context of Stéphane Dion's election as Liberal opposition leader and the emergence of climate change as a dominant political and policy issue. This volume focuses on Quebec-Canada relations and federal-provincial fiscal imbalance. Contributors explore several key policy and expenditure issues, including Canada-U.S. relations, the Federal Accountability Act, energy policy, health care, child care, crime and punishment, consumer policy, and public service labour relations. They also offer a critical analysis of the challenges to overall governance, including ministerial responsibility, public-private partnerships, and the handling of long-term spending commitments inherited by succeeding governments. Contributors include Timothy Barkiw (Toronto Metropolitan University), Gerard Boychuk (Waterloo), Keith Brownsey (Mount Royal College, Calgary), Peter Graefe (McMaster), Geoffrey Hale (Lethbridge), Carey Hill (Western Ontario), Ruth Hubbard (Ottawa), Derek Ireland (PhD student, Carleton), Rachel Laforest (Queen's), Ian Lee (Carleton), Trevor Lynn (Saskatchewan), Jonathan Malloy (Carleton), Scott Millar (Government of Canada), Gilles Paquet (emeritus, Ottawa), Michael Prince (Victoria), Christopher Stoney (Carleton), Gene Swimmer (Carleton), Katherine Teghtsoonian (Victoria), Andrew Teliszewsky (Ontario Minister of Health Promotion), Lori Turnbull (Dalhousie), and Kernaghan Webb (Toronto Metropolitan University).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".